class_docstr: Module that disentangle a fast-moving object from the background and performs deblurring.
  
  This is based on the original code from paper "DeFMO: Deblurring and Shape Recovery
      of Fast Moving Objects". See :cite:`DeFMO2021` for more details.
  
  Args:
      pretrained: Download and set pretrained weights to the model. Default: false.
  Returns:
      Temporal super-resolution without background.
  Shape:
      - Input: (B, 6, H, W)
      - Output: (B, S, 4, H, W)
  
  Examples:
      >>> import kornia
      >>> input = torch.rand(2, 6, 240, 320)
      >>> defmo = kornia.feature.DeFMO()
      >>> tsr_nobgr = defmo(input) # 2x24x4x240x320
code: def forward(self, input_data: Tensor) -> Tensor:
      latent = self.encoder(input_data)
      x_out = self.rendering(latent)
      return x_out
  
imports: from typing import Callable, Dict, List, Optional, Type
  import torch
  from torch import nn
  from kornia.core import Module, Tensor, concatenate, stack
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 2 for calling the DeFMO obj's underlying member method code with example usage: obj.forward(input_data) and returns: x_out